This invention relates generally to methods and systems for imaging, and more particularly to methods and systems for automatically determining regions in a scanned object, particularly using medical imaging systems.
Imaging systems are typically used to scan objects, and often are used to identify regions of interest within the objects. For example, in certain known computed tomography (CT) imaging systems, an x-ray source transmits x-ray beams through an object of interest. The x-ray beams pass through the object being imaged, such as a patient. The beams, after being attenuated by the object, impinge upon an array of radiation detectors. The intensity of the attenuated beam radiation received at the detector array is dependent upon the attenuation of the x-ray beam by the object. Each detector element of the array produces a separate electrical signal that is a measurement of the beam attenuation at the detector location. Attenuation measurements from the detectors are acquired separately for each detector element and collectively define a projection data set or transmission profile.
The x-ray source and the detector array may be rotated on a gantry within an imaging plane around the object to be imaged such that the angle at which the x-ray beam intersects the object constantly changes. A group of x-ray attenuation measurements (e.g., projection data set), from the detector array at one gantry angle is referred to as a “view”. A “scan” of the object comprises a set of views made at different gantry angles, or view angles, during one revolution of the x-ray source and detector. The projection data sets are processed to construct images that correspond to two-dimensional slices taken through the object at various angles. One exemplary method for forming an image from a projection data set is referred to as a filtered back projection technique.
Obtaining an optimum scan, for example, an optimum CT scan at the lowest possible dose relies on patient dependent information. X-ray flux management systems are known for obtaining patient dependent information in order to determine various operating parameters for scanning, such as the proper tube current, bowtie filter, and patient centering. Other information such as where to scan the patient must be determined by the technologist and be manually entered into the system in some manner. Generally, this manual operation is performed using a graphic Rx display by marking locations with a mouse and cursor on a scout image. A scout image is a radiographic projection image of the object that is obtained with the x-ray tube at a fixed stationary position while the object is translated in the Z axis. Marking locations on the scout image can be a very time consuming and complex process that is dependent on the experience of the technologist. This not only reduces scanning throughput, but can increase errors, for example, based on errors in the judgment of the technologist.
In addition, many potential dose management features are currently not practical or would be awkward and time consuming for the user because of the need to identify the required anatomic information. For example, dose reduction for dose sensitive organs is currently not available on CT scanners. One reason is that the technologist must identify and manually mark the location of sensitive organs such as breast, thyroid, eyes, uterus, etc. on a graphic Rx display. Size adjusted noise index and patient centering are ideally determined using the mean scout projections that are averaged over only the trunk of the body (top of lungs to the hips). Results can be adversely affected if portions of the neck or legs are included in the average, which can occur using known manual marking methods. Further, the radiologist may want to use different scan parameters for different regions of a helical scan. For example, in a chest-abdomen protocol a higher noise index may be desired for the lungs because nodule lesions have more contrast compared to lesions in the liver. Different regions (such as the lungs) are manually identified on the graphic Rx display by a technologist and can result in including more anatomy than needed for the region of clinical interest, thereby increasing patient dose and tube loading.
Further, some regions of the anatomy may require special compute intensive image reconstruction pre-processing correction steps. For example, the head requires a correction to compensate for bone beam hardening and detector spectral errors that would otherwise produce artifacts in the presence of bone in the skull. This correction is very compute intensive as the computations require an iterative approach. Image reconstruction times would take too long if this correction were applied for every image. Some hospitals perform trauma scans in one pass that include both the head and body. In this case, correction may be performed on data that does not need the correction because the regions are not properly identified due to technologist errors in the manual identification process. Also, Effective Dose is a common metric that permits estimating biological risk and allows doses to be compared from different imaging modalities. There are many known ways to calculate Effective Dose, but all of the methods need to know the organs that have been exposed to x-ray so that the appropriate organ doses can be determined. Accurate organ doses require lengthy Monte Carlo simulations. To speed up calculations, organ doses can be characterized on an anthropomorphic phantom and deterministic equations developed to translate the exposure of a given body region to an organ dose estimate. Again, manual determinations and identification of the organs adds time to this process.
Thus, known methods for identifying regions in a scanned object are often time consuming and susceptible to human error. Further, this manual identification also increases the time and complexity of processing using the manually identified information.